Non-synchronous Trading and High-Frequency Beta

In this paper we consider the problem of estimating high-frequency beta of an asset return when the returns are subject to the effects of market microstructure. Specifically, we study the correlation between intraday log returns of two assets. Our investigation starts with the effect of non-synchronous trading on intraday log returns when the underlying return series follows a stationary time series model. We also study the effect of non-synchronous trading on the covariance of two asset returns. To overcome the impact of non-synchronous trading, we use Markov chain Monte Carlo methods to recover the underlying log return series based on the observed intraday data. We then define a high-frequency beta based on the recovered log return series and propose an efficient method to estimate the measure. We apply the proposed analysis to many midor small-cap stocks using the Trade and Quote Data of the New York Stock Exchange, and discuss implications of the results obtained.